This disclosure relates generally to diagnostic systems, and more particularly, to systems and methods for diagnosis of disease states.
Coronary artery disease (CAD) is a leading cause of death in the developed world. As will be appreciated, CAD is generally caused by the gradual buildup of fatty deposits in the coronary arteries (atherosclerosis), which slowly narrows the blood flow through the arteries. Eventually, diminished blood flow may cause chest pain (angina), shortness of breath or other symptoms. A complete blockage may cause a heart attack. Furthermore, CAD typically develops over an extended period of time, and hence may go virtually unnoticed until it produces a heart attack. Early detection is important in order to maintain the current 1-year relative survival rate after treatment of about 91%.
Coronary angiography enables the detection of blockages or obstructions. However, coronary angiography is an invasive exam, which would be prohibitive to be applied to a large asymptomatic population for the purpose of early detection of the disease. Although, coronary angiography is considered to be the “gold standard” for the detection of CAD, recently, data increasingly supports the importance of non-invasively assessing the functional definition of the severity and extent of the disease process.
Moreover, there exist several techniques for myocardial image analysis, where the techniques are typically configured to compare an image with a corresponding normal reference image and provide statistical deviations of the image from the normal reference image. Normal patient data is acquired from different patients having normal hearts under different categories, such as, but not limited to, a study type (stress/rest), a tracer, sex, or the like. Averaging all subjects in that particular category may generate a normal reference image corresponding to a particular category. These normal reference images may be stored in a database, generally referred to as a normal reference database or a normal database. Polar maps corresponding to an image may be generated and compared with a corresponding normal reference image. Contrasting regions in the polar map may indicate deviations from the normal reference database. For example, a numerical entry in a segment may indicate that stress uptake was outside normal limits in that segment. This numerical entry may correspond to a difference between rest and stress defect, for instance.
Unfortunately, the normal reference images generated by the presently available techniques fail to account for any deviations in the orientations of the anatomical organ, thereby resulting in diminished accuracy of diagnosis of disease states. Moreover, indices representative of statistical deviations of the image from the normal reference image may enable a clinician to only make a subjective call regarding the degree of severity of the disease.
As will be appreciated, different phases may be associated with various anatomical organs. By way of example, if the anatomical organ includes the heart, the different phases of the heart may include a systolic phase, a diastolic phase, and phases therebetween. The presently available techniques fail to include information regarding the different phases of an organ. Consequently, the presently available techniques fail to account for the different phases of the anatomical organs, thereby leading to diminished accuracy of diagnosis of disease states.
It may therefore be desirable to develop a design that allows enhanced diagnosis of disease states. More particularly, there exists a need for generating normal reference images that account for the different phases of an anatomical organ, thereby allowing enhanced comparison between images and the corresponding normal reference images, thereby enhancing clinical workflow. In addition, there is also a need for generating normal reference images that account for any deviations in orientation of an anatomical region of interest.